2 days ago
The Utility CIO's Dilemma: Finding Clarity In Data With AI
Abhay Gupta is cofounder and CEO of Bidgely, evolving energy analytics for utilities with the power of data and artificial intelligence.
The modern utility faces a multifaceted mission stretching far beyond keeping the lights on. Today, its purview extends to a complex web of initiatives aimed at fostering energy efficiency, electrification and ambitious decarbonization goals. Since these initiatives are often provided funding, the challenge for utilities isn't always the scarcity of resources but, rather, a fundamental question: With so many critical pathways forward, where do we begin, and how do we determine which programs to prioritize first?
While these questions can feel subjective, the answers may lie within data. The majority of utilities have already made investments in smart meters, and many are now bolstering their IT ecosystems by integrating data warehouse platforms (e.g., Snowflake, AWS Redshift) to create vast data lakes, or enterprise data platforms, that house the millions of data points they collect each day.
However, the raw and unprocessed data in these enterprise data platforms doesn't inherently provide utility CIOs with clear guidance on where to allocate funds for maximum impact. How can CIOs optimize their existing smart meter and data warehouse investments to make this data truly valuable across the business?
Transforming Raw Data Into Smart Data With AI
By layering AI onto raw data, utilities can derive much more granular and meaningful insights about the energy being consumed across the territory. Specifically, by training AI algorithms to extract behind-the-meter insights, utilities can gain valuable intelligence into usage for not only standard home appliances but also electric vehicles (EVs), solar and battery storage.
The best part is that utilities can easily add AI software to their existing cloud platforms to become one cohesive application. In my opinion, the next evolution lies in embedding these powerful tools directly within the data platforms themselves so that these insights are immediately accessible and unified to further amplify value.
The integration of GenAI takes this capability a step further. Now, CIOs and other executives can directly interact with their data by asking natural language questions like, "Which of my customers have the highest grid impact based on their household energy use?" or, 'How many of my customers have Level 1 EV chargers?' Utilities can ask similar questions about households with pool pumps or those that show signs of inefficient or degrading appliances.
This AI layer on top of enterprise data platforms unlocks a dual advantage for CIOs, providing comprehensive use case lists that fuel both foundational infrastructure development and targeted customer engagement.
Connecting Insight With Programmatic Action
From a programmatic standpoint, this is the tip of the iceberg for utility CIOs to leverage specific program funding aimed at managing shiftable loads and improving efficiencies. Common programs include:
• Heat Pumps: Identifying homes with inefficient existing HVAC systems allows for targeted upgrade outreach and program enrollment.
• Pool Pumps: Pinpointing customers with single-speed pool pumps makes them prime candidates for upgrades to more flexible variable-speed models.
• EV Charging: Identifying customers with Level 1 chargers allows for targeted Level 2 charger upgrades. Similarly, analyzing each customer's charging patterns to detect on-peak charging enables targeted outreach for incentivized load-shifting.
For example, EVs highlight the limitations of static load shifting, where shifting charging from peak hours (e.g., 5 p.m. to 9 p.m.) to off-peak times (e.g., after 11 p.m.) can create new demand peaks. To address this, utilities are realizing they need a more dynamic approach to demand flexibility that integrates efforts across multiple initiatives to achieve wider impact.
Integrated Program Management
By automating routine program management (customer outreach, data collection, performance monitoring), AI tools free utility staff from administrative burdens to manage more programs concurrently without increasing operational complexity.
Because AI can also seamlessly integrate data from different programs and systems, utilities gain a more holistic view of their overall portfolio of initiatives and enable integrated program management. By understanding the interdependencies between programs, CIOs can better identify opportunities for synergy and optimization across multiple efforts.
For example, AI can evaluate how participation in a smart thermostat program impacts the effectiveness of a time-of-use rate program. Or, let's say you've just invested millions of dollars to get a distributed energy resource management system (DERMS) solution up and running. Knowing which customers have EVs or inefficient appliances would be extremely helpful in driving load-shifting strategies using that DERMS solution. With an AI layer, you gain the ability to precisely target and engage the right customers, unlocking the full potential of your DERMS investment.
With multiple programs underway simultaneously, CIOs can use these same AI tools for real-time monitoring and evaluation to identify potential issues early on and/or make data-driven adjustments to optimize program effectiveness across the board.
Despite all of this, AI's widespread integration remains a work in progress. While many are already embracing these benefits, some utilities I speak with remain hesitant for a few reasons. Chief among these are traditional organizational cultures, where resistance to change prevents investing significant upfront costs in a perceived "nascent" technology.
In an inherently risk-averse industry, this is not uncommon, and it's something we witnessed at the start of the smart meter transition. As with smart meters, however, the more willing the industry becomes to accept digital advancements, the faster these results start speaking for themselves.
There's also a critical talent gap of scarce and highly specialized AI data scientists and engineers that utilities feel they need to fill before integrating AI. Through GenAI, however, we're learning utilities can upskill their current workforce to turn any employee (even those without data science backgrounds) into AI experts. Instead of needing to write code or understand intricate data structures, employees can ask simple, conversational questions to analyze critical data insights.
The AI Advantage
In the complex landscape of utility initiatives and resource allocation, CIOs are discovering that AI is a powerful enabler to make strategic prioritization decisions while also providing the tools to efficiently manage a diverse portfolio of programs. Ultimately, utilities are better positioned to pursue a more comprehensive and effective approach to energy management and customer engagement.
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